人体运动数据实时捕捉系统的设计与实现

采用电子测量方法进行人体运动数据实时捕捉,构建人体运动数据实时捕捉系统,系统包括硬件设计和软件设计两部分,采用HP E1433A高速数据捕获总线模块记录人体运动数据并存储到数据硬盘中。进行运动数据捕捉的发射机、接收机、控制器等硬件构件设计,软件设计通过PXI实时系统触发各通道实现数据同步采集和回放,进行数据可视化模块设计和数据触发设置。最后进行系统调试,结果表明,采用该数据捕捉系统能实时进行人体数据的记录和输出,系统的稳定性较好。     

Action recognition on motion capture data using a dynemes and forward differences representation

In this paper we introduce a novel method for action/movement recognition in motion capture data. The joints orientation angles and the forward differences of these angles in different temporal scales are used to represent a motion capture sequence. Initially K-means is applied on training data to discover the most representative patterns on orientation angles and their forward differences. A novel K-means variant that takes into account the periodic nature of angular data is applied on the former. Each frame is then assigned to one or more of these patterns and histograms that describe the frequency of occurrence of these patterns for each movement are constructed. Nearest neighbour and SVM classification are used for action recognition on the test data. The effectiveness and robustness of this method is shown through extensive experimental results on four standard databases of motion capture data and various experimental setups.     

Automatic selection of frequency and time intervals for classification of EEG signals

A novel technique is presented for the automatic selection of time and frequency intervals to be used in feature extraction on multidimensional signals acquired by an electroencephalogram (EEG). This technique is completely automatic, adaptive (task independent), and does not require any specific prior domain knowledge. Experimental results obtained by integrating the proposed technique in a system for brain computer interface (BCI) confirm its effectiveness.     

基于动作捕捉技术的畲族舞蹈动态造型的信息化分析

基于动作捕捉技术的畲族舞蹈动态造型的分析与研究,不同于通过传统DV拍摄方式的研究,它通过标记的搜索、舞者姿态数据获取、舞者姿态数据曲线形成、自遮挡的处理及数据修正等步骤精确记录真实舞者各关节的三维运动轨迹,实现动作的到点分析,有利于真实地保存每组畲族舞蹈的艺术精华,有利于为今后对畲族舞蹈的研究、创编和影视还原提供精准的数字平台,也为作为省非物质文化遗产的代表作名录的畲族舞蹈的保护开辟一个新的领域,对于民族文化的保护和科学化发展有着深远的意义。     

基于HBase数据分类的压缩策略选择方法

为解决现有的HBase数据压缩策略选择方法未考虑数据的冷热性,以及在选择过程中存在片面性和不可靠性的缺陷,提出了基于HBase数据分类的压缩策略选择方法。依据数据文件的访问频度将HBase数据划分为冷热数据,并限定具体的访问级别;在此基础上增加评估层,综合考虑基于相邻区和统计列的选择方法,提出基于数据访问级别的压缩策略选择方法。仿真实验及结果表明,提出的压缩策略选择方法不仅节省了存储空间,还大大提高了数据查询的性能。     

Denial of service detection using dynamic time warping

With the rapid growth of security threats in computer networks, the need for developing efficient security-warning systems is substantially increasing. Distributed denial-of-service (DDoS) and DoS attacks are still among the most effective and dreadful attacks that require robust detection. In this work, we propose a new method to detect TCP DoS/DDoS attacks. Since analyzing network traffic is a promising approach, our proposed method utilizes network traffic by decomposing the TCP traffic into control and data planes and exploiting the dynamic time warping (DTW) algorithm for aligning these two planes with respect to the minimum Euclidean distance. By demonstrating that the distance between the control and data planes is considerably small for benign traffic, we exploit this characteristic for detecting attacks as outliers. An adaptive thresholding scheme is implemented by adjusting the value of the threshold in accordance with the local statistics of the median absolute deviation (MAD) of the distances between the two planes. We demonstrate the efficacy of the proposed method for detecting DoS/DDoS attacks by analyzing traffic data obtained from publicly available datasets.     

An enhanced feature selection filter for classification of microarray cancer data

The main aim of this study is to select the optimal set of genes from microarray cancer datasets that contribute to the prediction of specific cancer types. This study proposes the enhancement of the feature selection filter algorithm based on Joe's normalized mutual information and its use for gene selection. The proposed algorithm is implemented and evaluated on seven benchmark microarray cancer datasets, namely, central nervous system, leukemia (binary), leukemia (3 class), leukemia (4 class), lymphoma, mixed lineage leukemia, and small round blue cell tumor, using five well‐known classifiers, including the naive Bayes, radial basis function network, instance‐based classifier, decision‐based table, and decision tree. An average increase in the prediction accuracy of 5.1% is observed on all seven datasets averaged over all five classifiers. The average reduction in training time is 2.86 seconds. The performance of the proposed method is also compared with those of three other popular mutual information–based feature selection filters, namely, information gain, gain ratio, and symmetric uncertainty. The results are impressive when all five classifiers are used on all the datasets.     

Probabilistic inference of multijoint movements, skeletal parameters and marker attachments from diverse motion capture data

This paper describes a comprehensive solution to the problem of reconstructing the multijoint movement trajectories of the human body from diverse motion capture data. The problem is formulated in a probabilistic framework so as to handle multiple and unavoidable sources of uncertainty: sensor noise, soft tissue deformation and marker slip, inaccurate marker placement and limb measurement, and missing data due to occlusions. All unknown quantities are treated as state variables even though some of them are constant. In this way, state estimation and system identification can be performed simultaneously, obtaining not only the most likely values but also the confidence intervals of the joint angles, skeletal parameters, and marker positions and orientations relative to the limb segments. The inference method is a Gauss-Newton generalization of the extended Kalman filter. It is adapted to the kinematic domain by expressing spatial rotations via quaternions and computing the sensor residuals and their Jacobians analytically. The ultimate goal of this project is to provide a reliable data analysis tool used in practice. The software implementation is available online.     

运动捕捉技术在影视动画制作中的应用研究

运动捕捉技术是影视动画制作中的新技术。在对运动捕捉技术分析的基础上，讨论了基于光学运动捕捉技术制作动画的原理和方法，并详细描述了标记点跟踪和三雏重建等技术在影视动画制作过程中的应用。     

Improved motion estimation time using a combination of dynamic reference frame selection and residue-based mode decision

Motion estimation using multiple reference frames is widely used as the basis for recent video coding standards (eg. H.264/AVC) to achieve increased coding efficiency. However, this increases the complexity of the encoding process. In this paper, a new technique for efficient motion estimation is proposed. A combination of multiple reference frame selection and image residue-based mode selection is used to improve motion estimation time. By dynamic selection of an initial reference frame in advance, the number of reference frames to be considered is reduced. In addition, from examination of the residue between the current block and reconstructed blocks in preceding frames, variable block size mode decisions are made. Modified initial motion vector estimation and early stop condition detection are also adopted to speed up the motion estimation procedure. Experimental results compare the performance of the proposed algorithm with a state of the art motion estimation algorithm and demonstrate significantly reduced motion estimation time while maintaining PSNR performance.     

Fault detection in configurable switched-capacitor filters using transient analysis and dynamic time warping

In this work, we address a low-cost test of switched capacitors filters embedded in configurable analog sections. The proposal improves the Transient Analysis Method (TRAM) by incorporating a similarity measure, dynamic time warping. In this way, we extend TRAM to cases that that initially were not compatible and simplify the test of filters of order higher than two. This paper performs the test evaluation by developing a new simulation model of the addressed system that supports fault injection and simulation. A comparison with experimental data in both normal and faulty behavior validates the model. We consider catastrophic faults in the switches (stuck at open and short) and capacitors (shorts and opens), and deviation faults in the capacitors. The fault simulation results validate the test proposed here.

     

On minimizing memory in systolic arrays for the dynamic time warping algorithm

New systolic architectures have been evaluated for the Dynamic Time Warping (DTW) algorithm. This algorithm is a non-linear pattern matching technique used in isolated and continuous speech recognition systems.The proposed systolic decomposition for the DTW algorithm combines simultaneously (1) simple and regular systolic communication schemes and (2) a decomposition strategy which aims at a minimum amount of memory.This approach has led to a systolic architecture which is relatively flexible, compact and easy to test. Several arrays can be built up by keeping the same decomposition of the DTW-algorithm. This allows an easy exchange, depending on the desired application, of execution speed against chip-area.     

Waveform selection in radar target classification

We apply a sequential experiment design procedure to the problem of signal selection for radar target classification. Radar waveforms are designed to discriminate between targets possessing a doubly spread reflectivity function that are observed in clutter. The waveforms minimize decision time by maximizing the discrimination information in the echo signal. Each waveform selected maximizes the Kullback-Leibler (1951) information number that measures the dissimilarity between the observed target and the alternative targets. We discuss in details two scenarios. In the first scenario, the target environment is assumed fixed during illumination. In this case, the optimal waveform selection strategy leads to a fixed library of waveforms. During actual classification, the sequence in which the waveforms are selected from the library is determined from the noise to clutter power in the range-Doppler support of the targets. In the second scenario, the target environment changes between pulse transmissions. In this case, the maximum discrimination information is obtained by a repeated transmission of a single waveform designed from the reflectivity function of the targets. We show that our choice of signals can produce significant gains in detection performance     

Automatic selection of reference data for use in a nearest-neighbor method of pattern classification (Corresp.)

Two methods for automatic selection of nearest-neighbor reference data have been compared in small-scale experimentation. One of the methods is original and appears to be more successful than Hart's well-known "condensed nearest-neighbor" method [1].     

运动数据中的最优关联数据的分类分析

针对传统的分类方法在对运动数据的最优关联数据进行分类时,存在分类误差大、效率低的问题,提出基于决策树寻优的运动数据中的最优关联数据的分类算法。采用有向图模型构建运动数据的最优关联数据分布式结构模型,对采集的运动数据进行非线性时间序列特征重构,在重构的运动数据高维特征空间中进行运动数据的关联性映射,提取反映运动数据类别的属性特征,通过决策树寻优方法实现运动数据的自适应分类。仿真结果表明,采用该方法进行运动数据中的最优关联数据分类,其准确性较好、误分率较低,从而有效挖掘运动数据的关联信息,实现运动特征监测。     

运动数据中的最优关联数据的分类分析

针对传统关联数据分类方法一直存在分类精度差的问题,提出一种运动数据中的最优关联数据的分类方法。由于运动数据的变化量十分的庞大,同时变化程度也无法用规律进行推导,因此需要确定运动数据中的最优关联数据,通过最优关联数据的关联性进行高速提取,使用异导函数对提取的数据进行系统的分类。为了保证提出的运动数据中的最优关联数据的分类方法的有效性,设计对比仿真实验,通过实验数据表明,提出的运动数据中的最优关联数据的分类方法能够准确地对运动数据中的最优关联数据进行系统分类。     

运动捕获技术在电力安全仿真培训中的应用

为了满足电力企业对员工业务能力的要求,文中提出了一种基于虚拟现实技术的光学运动捕获系统。通过电力模拟单元、ZigBee技术和OpenGL图形库建立电力设备模型、虚拟操作环境和人体模型。利用人体模型驱动算法和卡尔曼滤波算法解决了特征点缺失的问题。实验结果表明,实际运动捕获和实时动画显示的跟踪过程中,采用卡尔曼滤波算法提取特征点更为准确,3D坐标的平均绝对误差(MAD)的平均值为1. 61mm,平均相对误差(MRE)的平均值为2. 23%,该系统能够提高受训者的体验和沉浸感。     

基于FPGA的肢体动作捕捉系统设计与实现

基于DE2-115开发平台,设计了一种高效的肢体动作捕捉系统.系统对摄像头采集到的视频信号进行肤色检测、噪声消除,提取出合适合适肤色块提取,在此基础上计算跟踪肢体质心坐标,并根据人体轮廓和质心之间的位置关系建立人体肢体模型.系统实现了肢体动作实时捕捉,并完成了三维模型重建.     

Parsimonious model selection for tissue segmentation and classification applications: a study using simulated and experimental DTI data

One aim of this work is to investigate the feasibility of using a hierarchy of models to describe diffusion tensor magnetic resonance (MR) data in fixed tissue. Parsimonious model selection criteria are used to choose among different models of diffusion within tissue. Using this information, we assess whether we can perform simultaneous tissue segmentation and classification. Both numerical phantoms and diffusion weighted imaging (DWI) data obtained from excised pig spinal cord are used to test and validate this model selection framework. Three hierarchical approaches are used for parsimonious model selection: the Schwarz criterion (SC), the F-test t-test (F- t), proposed by Hext, and the F-test F-test (F-F), adapted from Snedecor. The F - t approach is more robust than the others for selecting between isotropic and general anisotropic (full tensor) models. However, due to its high sensitivity to the variance estimate and bias in sorting eigenvalues, the F-F and SC are preferred for segmenting models with transverse isotropy (cylindrical symmetry). Additionally, the SC method is easier to implement than the F - t and F - F methods and has better performance. As such, this approach can be efficiently used for evaluating large MRI data sets. In addition, the proposed voxel-by-voxel segmentation framework is not susceptible to artifacts caused by the inhomogeneity of the variance in neighboring voxels with different degrees of anisotropy, which might contaminate segmentation results obtained with the techniques based on voxel averaging.